5+ years of production ML experience in risk, fraud, credit, or trust & safety applications
Hands-on experience with knowledge graph applications for business identity verification
Background in entity resolution across noisy and conflicting data sources
Expertise in handling real-world ML challenges like imbalanced labels and cold starts
Experience with end-to-end ML infrastructure including feature stores and model management
Responsibilities
Build ML applications in fraud and compliance with measurable customer impact
Address complex data challenges related to class imbalance and sparse signals
Innovate feature engineering and automation in labeling processes
Establish foundational ML infrastructure for multiple use cases
Design knowledge graph solutions leveraging LLMs for better entity resolution
Benefits
Hybrid work model with 2 days a week in the office
Strong emphasis on in-person collaboration
Support for flexible work arrangements
Opportunities for mentoring and setting technical direction
Full Job Description
About The Role:
We are actively building AI-driven applications that streamline customer workflows, focusing on business onboarding. With our proprietary identity data assets and deep domain expertise, we are uniquely positioned to expand into a broader set of AI-powered solutions that drive long-term growth.
We're looking for a hands-on applied ML expert to help build the technical foundation for these efforts. Ideally you have shipped external-facing models in the risk/fraud space and know the messy realities of imbalanced data, low labels, and changing behavior. This is a highly technical, hands-on role with wide influence on how we design, build, and scale ML at Middesk.
We follow a hybrid work model, and for this role, there is an expectation of 2 days per week in our SF/NYC office. Candidates should be based within a commutable distance, as we believe in the value of in-person collaboration and building strong team connections while also supporting flexibility where possible.
What You'll Do:
Build risk & fraud ML applications: Deliver production ML models in fraud, trust & safety, KYB, and compliance domains, with measurable impact on customer workflows.
Tackle hard data problems: Work on classification problems with extreme class imbalance, sparse signals, and "cold start" label challenges.
Innovate in feature engineering & labeling: Use graph-based techniques, weak supervision, LLMs, and AI agents to improve signal extraction and automate labeling process.
Establish ML infrastructure foundations: Partner with the ML infra team to design feature services, model training pipeline, model serving standards, and orchestration to scale multiple ML use cases.
Design and implement knowledge graph solutions: Leveraging LLMs for graph construction, querying, and retrieval to enhance entity resolution and business identity use cases.
What We're Looking For:
5+ years of production ML experience in one or more of the following areas:
Building Production ML for risk, fraud, credit, or trust & safety: Track record of shipping external-facing ML applications in one or more of these domains.
Knowledge graph applications: Hands-on experience building, querying, or extracting signals from knowledge graphs-ideally over business entity networks (companies, persons, addresses, relationships) to support identity verification, fraud detection, or risk decisioning.
Entity resolution for business or individual identities: Experience disambiguating and linking records across noisy, incomplete, or conflicting data sources-particularly in KYB, KYC, AML, or identity verification contexts where the same real-world entity may appear under different names, addresses, or tax IDs.
Expertise in classification with real-world ML challenges, for example: imbalanced labels, sparse signals, cold start, and production version management.
Hands-on ML infrastructure experience: feature stores, model management, ML training/serving pipelines.
Comfort as a senior IC: setting technical direction, mentoring peers, and establishing best practices.
Nice-To Have:
B2B SaaS experience, ideally building ML products for enterprise customers.
ML pipeline and automation engineering: Experience building end-to-end training harnesses that automate feature engineering, data validation, and model training.
Experience scaling ML across multiple products or risk domains.
About Middesk
Middesk is a San Francisco-based company that provides a platform for businesses to verify the legitimacy of other businesses. The company was founded in 2018 by Kyle Mack and Kurt Ruppel. Middesk's platform uses machine learning algorithms to analyze public data and provide insights into a company's legitimacy, such as its legal structure, ownership, and financial health. The company's customers include banks, insurance companies, and other businesses that need to verify the legitimacy of their partners and vendors.
